Managing Solution Architect

Capgemini • Chicago, IL

4d • Hybrid

About The Position

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world. We are seeking a highly technical, hands-on, and implementation-focused Solution Architect for Cloud, Data Analytics and AI. In this role, your primary focus will be implementation and end-to-end technical execution. You will be the solution architect, designing architecture blueprints suggesting right fit technologies for solving complex technical problems from clients in areas of Data, Cloud and AI. You will be guiding team to write code, build prototypes, and directly guiding development teams to turn complex system designs into stable, scalable production grade solutions.

Requirements

• Minimum of 14 years of experience in the IT industry.
• Minimum of 6 years of dedicated experience acting in an Architecture capacity.
• Bachelor’s or master’s degree in computer science, Information Systems, or a closely related technology field
• Expert-level, hands-on programming proficiency in Spark, Scala, and Java within major hyperscaler environments (AWS, Azure, or Google Cloud).
• Deep practical implementation experience with AI ecosystems like AWS Bedrock, AWS SageMaker, Google Vertex AI, Azure ML, and OpenAI.
• Mastery of building functional pipelines featuring prompt engineering, LLM fine-tuning, Agent Mesh, RAG, Vector Databases, Chain-of-Thought, Context Engineering, Loop Engineering, and MLOps/LLMOps.
• Direct facility with AI developer productivity toolsets (e.g., Cursor, Codex, Claude Code).
• Hands on implementation knowledge of LLM fine tuning, context engineering, Vector Databases Design, Model Routing, LLM Orchestration, Developing Agents and building Agentic Mesh, technologies such as LangChain, LlamaIndex, PineCone, Milvus, PyTorch, Tensor Flow and different transformer models including Claude, Gemini, OpenAI models, and ability to optimize token usage etc. are crucial for this role.
• Strong data engineering experience with cross-platform analytical data warehouses and data lakes houses, specifically Python, Spark, Big Query, Redshift, Synapse, Databricks or Snowflake.
• The candidate should have exposure to different styles of database modeling relational systems and modern NoSQL databases.
• Experience in developing microservices, event-driven platforms, and streaming/batch pipelines (Glue, Data Factory, DataFlow, Composer, Airflow, Kafka, Flink, or equivalent cloud services).
• Experience in Data Governance tools for data quality, data lineage, observability, and data marshalling, with preference for practical tooling validation using Informatica, Alation, Collibra, or Reltio will be valuable.
• Direct experience structuring real-world semantic layers, creating reusable data products, managing secure data shares, will be valuable.
• Awareness about secure network layout, infrastructure security controls, fault-tolerant/resilient cloud architecture setups, and FinOps scripting for resource scaling and cost optimization will be valuable.
• Deep fluency in DataOps, DevOps, and delivery pipeline automation, with direct hands-on configuration experience using GitHub Actions, Jenkins, Terraform, and git version management within fast-paced Agile environments are required.

Nice To Haves

• Industry awareness across one or more sectors is highly valued: Manufacturing, Automotive, Life Sciences, Telecommunications, Media, Hi-Tech, or Energy & Utilities.
• Active, professional cloud certifications are highly desired.
• While vibe coding knowledge is good to have, hands on implementation knowledge of LLM fine tuning, context engineering, Vector Databases Design, Model Routing, LLM Orchestration, Developing Agents and building Agentic Mesh, technologies such as LangChain, LlamaIndex, PineCone, Milvus, PyTorch, Tensor Flow and different transformer models including Claude, Gemini, OpenAI models, and ability to optimize token usage etc. are crucial for this role.
• Exposure or working knowledge in Graph database will be valuable.
• Preference for practical tooling validation using Informatica, Alation, Collibra, or Reltio will be valuable.
• Direct experience structuring real-world semantic layers, creating reusable data products, managing secure data shares, will be valuable.
• Awareness about secure network layout, infrastructure security controls, fault-tolerant/resilient cloud architecture setups, and FinOps scripting for resource scaling and cost optimization will be valuable.

Responsibilities

• Drive the day-to-day coding, technical builds, and engineering delivery of end-to-end data and AI platforms, working closely with Capgemini’s development team and clients.
• Partner directly with client delivery teams to execute complex platform migrations, cloud migrations, and tech-stack modernization projects across Data, Analytics, and AI domains.
• Build, scale, and realize detailed enterprise blueprints, translating conceptual patterns like Data Lake Medallion, event-driven, domain-driven, and modular microservices into functional application stacks.
• Support presales activities by creating rapid technical prototypes, conducting proof-of-concept (POC) builds, and architecting specific implementation pricing engines based on deep technical realities.
• Collaborate directly with technical leads from our alliance ecosystem-such as AWS, Microsoft, Google, Snowflake, Databricks, and Anthropic to know their latest features.
• Lead sprint teams from an engineering perspective, taking full ownership of deployment pipelines, complex environment configurations, and the real-time resolution of critical blockages during development phases.

Benefits

• Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade
• Company paid holidays
• Personal Days
• Sick Leave
• Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
• Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
• Life and disability insurance
• Employee assistance programs
• Other benefits as provided by local policy and eligibility

What This Job Offers

• Job Type: Full-time
• Career Level: Senior